👤 Songqin Liu

🔍 Search 📋 Browse 🏷️ Tags ❤️ Favourites ➕ Add 🧬 Extraction
3182
Articles
1983
Name variants
Also published as: A Liu, Ai Liu, Ai-Guo Liu, Aidong Liu, Aiguo Liu, Aihua Liu, Aijun Liu, Ailing Liu, Aimin Liu, Allen P Liu, Aman Liu, An Liu, An-Qi Liu, Ang-Jun Liu, Anjing Liu, Anjun Liu, Ankang Liu, Anling Liu, Anmin Liu, Annuo Liu, Anshu Liu, Ao Liu, Aoxing Liu, B Liu, Baihui Liu, Baixue Liu, Baiyan Liu, Ban Liu, Bang Liu, Bang-Quan Liu, Bao Liu, Bao-Cheng Liu, Baogang Liu, Baohui Liu, Baolan Liu, Baoli Liu, Baoning Liu, Baoxin Liu, Baoyi Liu, Bei Liu, Beibei Liu, Ben Liu, Bi-Cheng Liu, Bi-Feng Liu, Bihao Liu, Bilin Liu, Bin Liu, Bing Liu, Bing-Wen Liu, Bingcheng Liu, Bingjie Liu, Bingwen Liu, Bingxiao Liu, Bingya Liu, Bingyu Liu, Binjie Liu, Bo Liu, Bo-Gong Liu, Bo-Han Liu, Boao Liu, Bolin Liu, Boling Liu, Boqun Liu, Bowen Liu, Boxiang Liu, Boxin Liu, Boya Liu, Boyang Liu, Brian Y Liu, C Liu, C M Liu, C Q Liu, C-T Liu, C-Y Liu, Caihong Liu, Cailing Liu, Caiyan Liu, Can Liu, Can-Zhao Liu, Catherine H Liu, Chan Liu, Chang Liu, Chang-Bin Liu, Chang-Hai Liu, Chang-Ming Liu, Chang-Pan Liu, Chang-Peng Liu, Changbin Liu, Changjiang Liu, Changliang Liu, Changming Liu, Changqing Liu, Changtie Liu, Changya Liu, Changyun Liu, Chao Liu, Chao-Ming Liu, Chaohong Liu, Chaoqi Liu, Chaoyi Liu, Chelsea Liu, Chen Liu, Chenchen Liu, Chendong Liu, Cheng Liu, Cheng-Li Liu, Cheng-Wu Liu, Cheng-Yong Liu, Cheng-Yun Liu, Chengbo Liu, Chenge Liu, Chengguo Liu, Chenghui Liu, Chengkun Liu, Chenglong Liu, Chengxiang Liu, Chengyao Liu, Chengyun Liu, Chenmiao Liu, Chenming Liu, Chenshu Liu, Chenxing Liu, Chenxu Liu, Chenxuan Liu, Chi Liu, Chia-Chen Liu, Chia-Hung Liu, Chia-Jen Liu, Chia-Yang Liu, Chia-Yu Liu, Chiang Liu, Chin-Chih Liu, Chin-Ching Liu, Chin-San Liu, Ching-Hsuan Liu, Ching-Ti Liu, Chong Liu, Christine S Liu, ChuHao Liu, Chuan Liu, Chuanfeng Liu, Chuanxin Liu, Chuanyang Liu, Chun Liu, Chun-Chi Liu, Chun-Feng Liu, Chun-Lei Liu, Chun-Ming Liu, Chun-Xiao Liu, Chun-Yu Liu, Chunchi Liu, Chundong Liu, Chunfeng Liu, Chung-Cheng Liu, Chung-Ji Liu, Chunhua Liu, Chunlei Liu, Chunliang Liu, Chunling Liu, Chunming Liu, Chunpeng Liu, Chunping Liu, Chunsheng Liu, Chunwei Liu, Chunxiao Liu, Chunyan Liu, Chunying Liu, Chunyu Liu, Cici Liu, Clarissa M Liu, Cong Cong Liu, Cong Liu, Congcong Liu, Cui Liu, Cui-Cui Liu, Cuicui Liu, Cuijie Liu, Cuilan Liu, Cun Liu, Cun-Fei Liu, D Liu, Da Liu, Da-Ren Liu, Daiyun Liu, Dajiang J Liu, Dan Liu, Dan-Ning Liu, Dandan Liu, Danhui Liu, Danping Liu, Dantong Liu, Danyang Liu, Danyong Liu, Daoshen Liu, David Liu, David R Liu, Dawei Liu, Daxu Liu, Dayong Liu, Dazhi Liu, De-Pei Liu, De-Shun Liu, Dechao Liu, Dehui Liu, Deliang Liu, Deng-Xiang Liu, Depei Liu, Deping Liu, Derek Liu, Deruo Liu, Desheng Liu, Dewu Liu, Dexi Liu, Deyao Liu, Deying Liu, Dezhen Liu, Di Liu, Didi Liu, Ding-Ming Liu, Dingding Liu, Dinglu Liu, Dingxiang Liu, Dong Liu, Dong-Yun Liu, Dongang Liu, Dongbo Liu, Dongfang Liu, Donghui Liu, Dongjuan Liu, Dongliang Liu, Dongmei Liu, Dongming Liu, Dongping Liu, Dongxian Liu, Dongxue Liu, Dongyan Liu, Dongyang Liu, Dongyao Liu, Dongzhou Liu, Dudu Liu, Dunjiang Liu, Edison Tak-Bun Liu, En-Qi Liu, Enbin Liu, Enlong Liu, Enqi Liu, Erdong Liu, Erfeng Liu, Erxiong Liu, F Liu, F Z Liu, Fan Liu, Fan-Jie Liu, Fang Liu, Fang-Zhou Liu, Fangli Liu, Fangmei Liu, Fangping Liu, Fangqi Liu, Fangzhou Liu, Fani Liu, Fayu Liu, Fei Liu, Feifan Liu, Feilong Liu, Feiyan Liu, Feiyang Liu, Feiye Liu, Fen Liu, Fendou Liu, Feng Liu, Feng-Ying Liu, Fengbin Liu, Fengchao Liu, Fengen Liu, Fengguo Liu, Fengjiao Liu, Fengjie Liu, Fengjuan Liu, Fengqiong Liu, Fengsong Liu, Fonda Liu, Foqiu Liu, Fu-Jun Liu, Fu-Tong Liu, Fubao Liu, Fuhao Liu, Fuhong Liu, Fujun Liu, Gan Liu, Gang Liu, Gangli Liu, Ganqiang Liu, Gaohua Liu, Ge Liu, Ge-Li Liu, Gen Sheng Liu, Geng Liu, Geng-Hao Liu, Geoffrey Liu, George E Liu, George Liu, Geroge Liu, Gexiu Liu, Gongguan Liu, Guang Liu, Guangbin Liu, Guangfan Liu, Guanghao Liu, Guangliang Liu, Guangqin Liu, Guangwei Liu, Guangxu Liu, Guannan Liu, Guantong Liu, Gui Yao Liu, Gui-Fen Liu, Gui-Jing Liu, Gui-Rong Liu, Guibo Liu, Guidong Liu, Guihong Liu, Guiju Liu, Guili Liu, Guiqiong Liu, Guiquan Liu, Guisheng Liu, Guiyou Liu, Guiyuan Liu, Guning Liu, Guo-Liang Liu, Guochang Liu, Guodong Liu, Guohao Liu, Guojun Liu, Guoke Liu, Guoliang Liu, Guopin Liu, Guoqiang Liu, Guoqing Liu, Guoquan Liu, Guowen Liu, Guoyong Liu, H Liu, Hai Feng Liu, Hai-Jing Liu, Hai-Xia Liu, Hai-Yan Liu, Haibin Liu, Haichao Liu, Haifei Liu, Haifeng Liu, Hailan Liu, Hailin Liu, Hailing Liu, Haitao Liu, Haiyan Liu, Haiyang Liu, Haiying Liu, Haizhao Liu, Han Liu, Han-Fu Liu, Han-Qi Liu, Hancong Liu, Hang Liu, Hanhan Liu, Hanjiao Liu, Hanjie Liu, Hanmin Liu, Hanqing Liu, Hanxiang Liu, Hanyuan Liu, Hao Liu, Haobin Liu, Haodong Liu, Haogang Liu, Haojie Liu, Haokun Liu, Haoling Liu, Haowei Liu, Haowen Liu, Haoyue Liu, He-Kun Liu, Hehe Liu, Hekun Liu, Heliang Liu, Heng Liu, Hengan Liu, Hengru Liu, Hengtong Liu, Heyi Liu, Hong Juan Liu, Hong Liu, Hong Wei Liu, Hong-Bin Liu, Hong-Li Liu, Hong-Liang Liu, Hong-Tao Liu, Hong-Xiang Liu, Hong-Ying Liu, Hongbin Liu, Hongbing Liu, Hongfa Liu, Honghan Liu, Honghe Liu, Hongjian Liu, Hongjie Liu, Hongjun Liu, Hongli Liu, Hongliang Liu, Hongmei Liu, Hongqun Liu, Hongtao Liu, Hongwei Liu, Hongxiang Liu, Hongxing Liu, Hongyan Liu, Hongyang Liu, Hongyao Liu, Hongyu Liu, Hongyuan Liu, Houbao Liu, Hsiao-Ching Liu, Hsiao-Sheng Liu, Hsiaowei Liu, Hsu-Hsiang Liu, Hu Liu, Hua Liu, Hua-Cheng Liu, Hua-Ge Liu, Huadong Liu, Huaizheng Liu, Huan Liu, Huan-Yu Liu, Huanhuan Liu, Huanliang Liu, Huanyi Liu, Huatao Liu, Huawei Liu, Huayang Liu, Huazhen Liu, Hui Liu, Hui-Chao Liu, Hui-Fang Liu, Hui-Guo Liu, Hui-Hui Liu, Hui-Xin Liu, Hui-Ying Liu, Huibin Liu, Huidi Liu, Huihua Liu, Huihui Liu, Huijuan Liu, Huijun Liu, Huikun Liu, Huiling Liu, Huimao Liu, Huimin Liu, Huiming Liu, Huina Liu, Huiping Liu, Huiqing Liu, Huisheng Liu, Huiying Liu, Huiyu Liu, Hulin Liu, J Liu, J R Liu, J W Liu, J X Liu, J Z Liu, James K C Liu, Jamie Liu, Jay Liu, Ji Liu, Ji-Kai Liu, Ji-Long Liu, Ji-Xing Liu, Ji-Xuan Liu, Ji-Yun Liu, Jia Liu, Jia-Cheng Liu, Jia-Jun Liu, Jia-Qian Liu, Jia-Yao Liu, JiaXi Liu, Jiabin Liu, Jiachen Liu, Jiahao Liu, Jiahua Liu, Jiahui Liu, Jiajie Liu, Jiajuan Liu, Jiakun Liu, Jiali Liu, Jialin Liu, Jiamin Liu, Jiaming Liu, Jian Liu, Jian-Jun Liu, Jian-Kun Liu, Jian-hong Liu, Jian-shu Liu, Jianan Liu, Jianbin Liu, Jianbo Liu, Jiandong Liu, Jianfang Liu, Jianfeng Liu, Jiang Liu, Jiangang Liu, Jiangbin Liu, Jianghong Liu, Jianghua Liu, Jiangjiang Liu, Jiangjin Liu, Jiangling Liu, Jiangxin Liu, Jiangyan Liu, Jianhua Liu, Jianhui Liu, Jiani Liu, Jianing Liu, Jianjiang Liu, Jianjun Liu, Jiankang Liu, Jiankun Liu, Jianlei Liu, Jianmei Liu, Jianmin Liu, Jiannan Liu, Jianping Liu, Jiantao Liu, Jianwei Liu, Jianxi Liu, Jianxin Liu, Jianyong Liu, Jianyu Liu, Jianyun Liu, Jiao Liu, Jiaojiao Liu, Jiaoyang Liu, Jiaqi Liu, Jiaqing Liu, Jiawen Liu, Jiaxian Liu, Jiaxiang Liu, Jiaxin Liu, Jiayan Liu, Jiayi Liu, Jiayin Liu, Jiaying Liu, Jiayu Liu, Jiayun Liu, Jiazhe Liu, Jiazheng Liu, Jiazhuo Liu, Jidan Liu, Jie Liu, Jie-Qing Liu, Jierong Liu, Jiewei Liu, Jiewen Liu, Jieying Liu, Jieyu Liu, Jihe Liu, Jiheng Liu, Jin Liu, Jin-Juan Liu, Jin-Qing Liu, Jinbao Liu, Jinbo Liu, Jincheng Liu, Jindi Liu, Jinfeng Liu, Jing Liu, Jing Min Liu, Jing-Crystal Liu, Jing-Hua Liu, Jing-Ying Liu, Jing-Yu Liu, Jingbo Liu, Jingchong Liu, Jingfang Liu, Jingfeng Liu, Jingfu Liu, Jinghui Liu, Jingjie Liu, Jingjing Liu, Jingmeng Liu, Jingmin Liu, Jingqi Liu, Jingquan Liu, Jingqun Liu, Jingsheng Liu, Jingwei Liu, Jingwen Liu, Jingxing Liu, Jingyi Liu, Jingying Liu, Jingyun Liu, Jingzhong Liu, Jinjie Liu, Jinlian Liu, Jinlong Liu, Jinman Liu, Jinpei Liu, Jinpeng Liu, Jinping Liu, Jinqin Liu, Jinrong Liu, Jinsheng Liu, Jinsong Liu, Jinsuo Liu, Jinxiang Liu, Jinxin Liu, Jinxing Liu, Jinyue Liu, Jinze Liu, Jinzhao Liu, Jinzhi Liu, Jiong Liu, Jishan Liu, Jitao Liu, Jiwei Liu, Jixin Liu, Jonathan Liu, Joyce F Liu, Joyce Liu, Ju Liu, Ju-Fang Liu, Juan Liu, Juanjuan Liu, Juanxi Liu, Jue Liu, Jui-Tung Liu, Jun Liu, Jun O Liu, Jun Ting Liu, Jun Yi Liu, Jun-Jen Liu, Jun-Yan Liu, Jun-Yi Liu, Junbao Liu, Junchao Liu, Junfen Liu, Junhui Liu, Junjiang Liu, Junjie Liu, Junjin Liu, Junjun Liu, Junlin Liu, Junling Liu, Junnian Liu, Junpeng Liu, Junqi Liu, Junrong Liu, Juntao Liu, Juntian Liu, Junwen Liu, Junwu Liu, Junxi Liu, Junyan Liu, Junye Liu, Junying Liu, Junyu Liu, Juyao Liu, Kai Liu, Kai-Zheng Liu, Kaidong Liu, Kaijing Liu, Kaikun Liu, Kaiqi Liu, Kaisheng Liu, Kaitai Liu, Kaiwen Liu, Kang Liu, Kang-le Liu, Kangdong Liu, Kangwei Liu, Kathleen D Liu, Ke Liu, Ke-Tong Liu, Kechun Liu, Kehui Liu, Kejia Liu, Keng-Hau Liu, Keqiang Liu, Kexin Liu, Kiang Liu, Kuangyi Liu, Kun Liu, Kun-Cheng Liu, Kwei-Yan Liu, L L Liu, L Liu, L W Liu, Lan Liu, Lan-Xiang Liu, Lang Liu, Lanhao Liu, Le Liu, Lebin Liu, Lei Liu, Lele Liu, Leping Liu, Li Liu, Li-Fang Liu, Li-Min Liu, Li-Rong Liu, Li-Wen Liu, Li-Xuan Liu, Li-Ying Liu, Li-ping Liu, Lian Liu, Lianfei Liu, Liang Liu, Liang-Chen Liu, Liang-Feng Liu, Liangguo Liu, Liangji Liu, Liangjia Liu, Liangliang Liu, Liangyu Liu, Lianxin Liu, Lianyong Liu, Libin Liu, Lichao Liu, Lichun Liu, Lidong Liu, Liegang Liu, Lifang Liu, Ligang Liu, Lihua Liu, Lijuan Liu, Lijun Liu, Lili Liu, Liling Liu, Limin Liu, Liming Liu, Lin Liu, Lina Liu, Ling Liu, Ling-Yun Liu, Ling-Zhi Liu, Lingfei Liu, Lingjiao Liu, Lingjuan Liu, Linglong Liu, Lingyan Liu, Lining Liu, Linlin Liu, Linqing Liu, Linwen Liu, Liping Liu, Liqing Liu, Liqiong Liu, Liqun Liu, Lirong Liu, Liru Liu, Liu Liu, Liumei Liu, Liusheng Liu, Liwen Liu, Lixia Liu, Lixian Liu, Lixiao Liu, Liying Liu, Liyue Liu, Lizhen Liu, Long Liu, Longfei Liu, Longjian Liu, Longqian Liu, Longyang Liu, Longzhou Liu, Lu Liu, Luhong Liu, Lulu Liu, Luming Liu, Lunxu Liu, Luping Liu, Lushan Liu, Lv Liu, M L Liu, M Liu, Man Liu, Man-Ru Liu, Manjiao Liu, Manqi Liu, Manran Liu, Maolin Liu, Mei Liu, Mei-mei Liu, Meicen Liu, Meifang Liu, Meijiao Liu, Meijing Liu, Meijuan Liu, Meijun Liu, Meiling Liu, Meimei Liu, Meixin Liu, Meiyan Liu, Meng Han Liu, Meng Liu, Meng-Hui Liu, Meng-Meng Liu, Meng-Yue Liu, Mengduan Liu, Mengfan Liu, Mengfei Liu, Menggang Liu, Menghan Liu, Menghua Liu, Menghui Liu, Mengjia Liu, Mengjiao Liu, Mengke Liu, Menglin Liu, Mengling Liu, Mengmei Liu, Mengqi Liu, Mengqian Liu, Mengxi Liu, Mengxue Liu, Mengyang Liu, Mengying Liu, Mengyu Liu, Mengyuan Liu, Mengzhen Liu, Mi Liu, Mi-Hua Liu, Mi-Min Liu, Miao Liu, Miaoliang Liu, Min Liu, Minda Liu, Minetta C Liu, Ming Liu, Ming-Jiang Liu, Ming-Qi Liu, Mingcheng Liu, Mingchun Liu, Mingfan Liu, Minghui Liu, Mingjiang Liu, Mingjing Liu, Mingjun Liu, Mingli Liu, Mingming Liu, Mingna Liu, Mingqin Liu, Mingrui Liu, Mingsen Liu, Mingsong Liu, Mingxiao Liu, Mingxing Liu, Mingxu Liu, Mingyang Liu, Mingyao Liu, Mingying Liu, Mingyu Liu, Minhao Liu, Minxia Liu, Mo-Nan Liu, Modan Liu, Mouze Liu, Muqiu Liu, Musang Liu, N A Liu, N Liu, Na Liu, Na-Nv Liu, Na-Wei Liu, Nai-feng Liu, Naihua Liu, Naili Liu, Nan Liu, Nan-Song Liu, Nana Liu, Nannan Liu, Nanxi Liu, Ni Liu, Nian Liu, Ning Liu, Ning'ang Liu, Ningning Liu, Niya Liu, Ou Liu, Ouxuan Liu, P C Liu, Pan Liu, Panhong Liu, Panting Liu, Paul Liu, Pei Liu, Pei-Ning Liu, Peijian Liu, Peijie Liu, Peijun Liu, Peilong Liu, Peiqi Liu, Peiqing Liu, Peiwei Liu, Peixi Liu, Peiyao Liu, Peizhong Liu, Peng Liu, Pengcheng Liu, Pengfei Liu, Penghong Liu, Pengli Liu, Pengtao Liu, Pengyu Liu, Pengyuan Liu, Pentao Liu, Peter S Liu, Piaopiao Liu, Pinduo Liu, Ping Liu, Ping-Yen Liu, Pinghuai Liu, Pingping Liu, Pingsheng Liu, Q Liu, Qi Liu, Qi-Xian Liu, Qian Liu, Qian-Wen Liu, Qiang Liu, Qiang-Yuan Liu, Qiangyun Liu, Qianjin Liu, Qianqi Liu, Qianshuo Liu, Qianwei Liu, Qiao-Hong Liu, Qiaofeng Liu, Qiaoyan Liu, Qiaozhen Liu, Qiji Liu, Qiming Liu, Qin Liu, Qinfang Liu, Qing Liu, Qing-Huai Liu, Qing-Rong Liu, Qingbin Liu, Qingbo Liu, Qingguang Liu, Qingguo Liu, Qinghao Liu, Qinghong Liu, Qinghua Liu, Qinghuai Liu, Qinghuan Liu, Qinglei Liu, Qingping Liu, Qingqing Liu, Qingquan Liu, Qingsong Liu, Qingxia Liu, Qingxiang Liu, Qingyang Liu, Qingyou Liu, Qingyun Liu, Qingzhuo Liu, Qinqin Liu, Qiong Liu, Qiu-Ping Liu, Qiulei Liu, Qiuli Liu, Qiulu Liu, Qiushi Liu, Qiuxu Liu, Qiuyu Liu, Qiuyue Liu, Qiwei Liu, Qiyao Liu, Qiye Liu, Qizhan Liu, Quan Liu, Quan-Jun Liu, Quanxin Liu, Quanying Liu, Quanzhong Liu, Quentin Liu, Qun Liu, Qunlong Liu, Qunpeng Liu, R F Liu, R Liu, R Y Liu, Ran Liu, Rangru Liu, Ranran Liu, Ren Liu, Renling Liu, Ri Liu, Rong Liu, Rong-Zong Liu, Rongfei Liu, Ronghua Liu, Rongxia Liu, Rongxun Liu, Rui Liu, Rui-Jie Liu, Rui-Tian Liu, Rui-Xuan Liu, Ruichen Liu, Ruihua Liu, Ruijie Liu, Ruijuan Liu, Ruilong Liu, Ruiping Liu, Ruiqi Liu, Ruitong Liu, Ruixia Liu, Ruiyi Liu, Ruizao Liu, Runjia Liu, Runjie Liu, Runni Liu, Runping Liu, Ruochen Liu, Ruotian Liu, Ruowen Liu, Ruoyang Liu, Ruyi Liu, Ruyue Liu, S Liu, Saiji Liu, Sasa Liu, Sen Liu, Senchen Liu, Senqi Liu, Sha Liu, Shan Liu, Shan-Shan Liu, Shandong Liu, Shang-Feng Liu, Shang-Xin Liu, Shangjing Liu, Shangxin Liu, Shangyu Liu, Shangyuan Liu, Shangyun Liu, Shanhui Liu, Shanling Liu, Shanshan Liu, Shao-Bin Liu, Shao-Jun Liu, Shao-Yuan Liu, Shaobo Liu, Shaocheng Liu, Shaohua Liu, Shaojun Liu, Shaoqing Liu, Shaowei Liu, Shaoying Liu, Shaoyou Liu, Shaoyu Liu, Shaozhen Liu, Shasha Liu, Sheng Liu, Shengbin Liu, Shengjun Liu, Shengnan Liu, Shengyang Liu, Shengzhi Liu, Shengzhuo Liu, Shenhai Liu, Shenping Liu, Shi Liu, Shi-Lian Liu, Shi-Wei Liu, Shi-Yong Liu, Shi-guo Liu, ShiWei Liu, Shih-Ping Liu, Shijia Liu, Shijian Liu, Shijie Liu, Shijun Liu, Shikai Liu, Shikun Liu, Shilin Liu, Shing-Hwa Liu, Shiping Liu, Shiqian Liu, Shiquan Liu, Shiru Liu, Shixi Liu, Shiyan Liu, Shiyang Liu, Shiying Liu, Shiyu Liu, Shiyuan Liu, Shou-Sheng Liu, Shouguo Liu, Shoupei Liu, Shouxin Liu, Shouyang Liu, Shu Liu, Shu-Chen Liu, Shu-Jing Liu, Shu-Lin Liu, Shu-Qiang Liu, Shu-Qin Liu, Shuai Liu, Shuaishuai Liu, Shuang Liu, Shuangli Liu, Shuangzhu Liu, Shuhong Liu, Shuhua Liu, Shui-Bing Liu, Shujie Liu, Shujing Liu, Shujun Liu, Shulin Liu, Shuling Liu, Shumin Liu, Shun-Mei Liu, Shunfang Liu, Shuning Liu, Shunming Liu, Shuqian Liu, Shuqing Liu, Shuwen Liu, Shuxi Liu, Shuxian Liu, Shuya Liu, Shuyan Liu, Shuyu Liu, Si-Jin Liu, Si-Xu Liu, Si-Yan Liu, Si-jun Liu, Sicheng Liu, Sidan Liu, Side Liu, Sihao Liu, Sijing Liu, Sijun Liu, Silvia Liu, Simin Liu, Sipu Liu, Siqi Liu, Siqin Liu, Siru Liu, Sirui Liu, Sisi Liu, Sitian Liu, Siwen Liu, Sixi Liu, Sixin Liu, Sixiu Liu, Sixu Liu, Siyao Liu, Siyi Liu, Siyu Liu, Siyuan Liu, Song Liu, Song-Fang Liu, Song-Mei Liu, Song-Ping Liu, Songfang Liu, Songhui Liu, Songsong Liu, Songyi Liu, Su Liu, Su-Yun Liu, Sudong Liu, Suhuan Liu, Sui-Feng Liu, Suling Liu, Suosi Liu, Sushuang Liu, Susu Liu, Szu-Heng Liu, T H Liu, T Liu, Ta-Chih Liu, Taihang Liu, Taixiang Liu, Tang Liu, Tao Liu, Taoli Liu, Taotao Liu, Te Liu, Teng Liu, Tengfei Liu, Tengli Liu, Teresa T Liu, Tian Liu, Tian Shu Liu, Tianhao Liu, Tianhu Liu, Tianjia Liu, Tianjiao Liu, Tianlai Liu, Tianlang Liu, Tianlong Liu, Tianqiang Liu, Tianrui Liu, Tianshu Liu, Tiantian Liu, Tianyao Liu, Tianyi Liu, Tianyu Liu, Tianze Liu, Tiemin Liu, Tina Liu, Ting Liu, Ting-Li Liu, Ting-Ting Liu, Ting-Yuan Liu, Tingjiao Liu, Tingting Liu, Tong Liu, Tonglin Liu, Tongtong Liu, Tongyan Liu, Tongyu Liu, Tongyun Liu, Tongzheng Liu, Tsang-Wu Liu, Tsung-Yun Liu, Vincent W S Liu, W Liu, W-Y Liu, Wan Liu, Wan-Chun Liu, Wan-Di Liu, Wan-Guo Liu, Wan-Ying Liu, Wang Liu, Wangrui Liu, Wanguo Liu, Wangyang Liu, Wanjun Liu, Wanli Liu, Wanlu Liu, Wanqi Liu, Wanqing Liu, Wanting Liu, Wei Liu, Wei-Chieh Liu, Wei-Hsuan Liu, Wei-Hua Liu, Weida Liu, Weifang Liu, Weifeng Liu, Weiguo Liu, Weihai Liu, Weihong Liu, Weijian Liu, Weijie Liu, Weijun Liu, Weilin Liu, Weimin Liu, Weiming Liu, Weina Liu, Weiqin Liu, Weiqing Liu, Weiren Liu, Weisheng Liu, Weishuo Liu, Weiwei Liu, Weiyang Liu, Wen Liu, Wen Yuan Liu, Wen-Chun Liu, Wen-Di Liu, Wen-Fang Liu, Wen-Jie Liu, Wen-Jing Liu, Wen-Qiang Liu, Wen-Tao Liu, Wen-ling Liu, Wenbang Liu, Wenbin Liu, Wenbo Liu, Wenchao Liu, Wenen Liu, Wenfeng Liu, Wenhan Liu, Wenhao Liu, Wenhua Liu, Wenjie Liu, Wenjing Liu, Wenlang Liu, Wenli Liu, Wenling Liu, Wenlong Liu, Wenna Liu, Wenping Liu, Wenqi Liu, Wenrui Liu, Wensheng Liu, Wentao Liu, Wenwu Liu, Wenxiang Liu, Wenxuan Liu, Wenya Liu, Wenyan Liu, Wenyi Liu, Wenzhong Liu, Wu Liu, Wuping Liu, Wuyang Liu, X C Liu, X Liu, X P Liu, X-D Liu, Xi Liu, Xi-Yu Liu, Xia Liu, Xia-Meng Liu, Xialin Liu, Xian Liu, Xianbao Liu, Xianchen Liu, Xianda Liu, Xiang Liu, Xiang-Qian Liu, Xiang-Yu Liu, Xiangchen Liu, Xiangfei Liu, Xianglan Liu, Xiangli Liu, Xiangliang Liu, Xianglu Liu, Xiangning Liu, Xiangping Liu, Xiangsheng Liu, Xiangtao Liu, Xiangting Liu, Xiangxiang Liu, Xiangxuan Liu, Xiangyong Liu, Xiangyu Liu, Xiangyun Liu, Xianli Liu, Xianling Liu, Xiansheng Liu, Xianyang Liu, Xiao Dong Liu, Xiao Liu, Xiao Yan Liu, Xiao-Cheng Liu, Xiao-Dan Liu, Xiao-Gang Liu, Xiao-Guang Liu, Xiao-Huan Liu, Xiao-Jiao Liu, Xiao-Li Liu, Xiao-Ling Liu, Xiao-Ning Liu, Xiao-Qiu Liu, Xiao-Qun Liu, Xiao-Rong Liu, Xiao-Song Liu, Xiao-Xiao Liu, Xiao-lan Liu, Xiaoan Liu, Xiaobai Liu, Xiaobei Liu, Xiaobing Liu, Xiaocen Liu, Xiaochuan Liu, Xiaocong Liu, Xiaodan Liu, Xiaoding Liu, Xiaodong Liu, Xiaofan Liu, Xiaofang Liu, Xiaofei Liu, Xiaogang Liu, Xiaoguang Liu, Xiaoguang Margaret Liu, Xiaohan Liu, Xiaoheng Liu, Xiaohong Liu, Xiaohua Liu, Xiaohuan Liu, Xiaohui Liu, Xiaojie Liu, Xiaojing Liu, Xiaoju Liu, Xiaojun Liu, Xiaole Shirley Liu, Xiaolei Liu, Xiaoli Liu, Xiaolin Liu, Xiaoling Liu, Xiaoman Liu, Xiaomei Liu, Xiaomeng Liu, Xiaomin Liu, Xiaoming Liu, Xiaona Liu, Xiaonan Liu, Xiaopeng Liu, Xiaoping Liu, Xiaoqian Liu, Xiaoqiang Liu, Xiaoqin Liu, Xiaoqing Liu, Xiaoran Liu, Xiaosong Liu, Xiaotian Liu, Xiaoting Liu, Xiaowei Liu, Xiaoxi Liu, Xiaoxia Liu, Xiaoxiao Liu, Xiaoxu Liu, Xiaoxue Liu, Xiaoya Liu, Xiaoyan Liu, Xiaoyang Liu, Xiaoye Liu, Xiaoying Liu, Xiaoyong Liu, Xiaoyu Liu, Xiawen Liu, Xibao Liu, Xibing Liu, Xie-hong Liu, Xiehe Liu, Xiguang Liu, Xijun Liu, Xili Liu, Xin Liu, Xin-Hua Liu, Xin-Yan Liu, Xinbo Liu, Xinchang Liu, Xing Liu, Xing-De Liu, Xing-Li Liu, Xing-Yang Liu, Xingbang Liu, Xingde Liu, Xinghua Liu, Xinghui Liu, Xingjing Liu, Xinglei Liu, Xingli Liu, Xinglong Liu, Xinguo Liu, Xingxiang Liu, Xingyi Liu, Xingyu Liu, Xinhua Liu, Xinjun Liu, Xinlei Liu, Xinli Liu, Xinmei Liu, Xinmin Liu, Xinran Liu, Xinru Liu, Xinrui Liu, Xintong Liu, Xinxin Liu, Xinyao Liu, Xinyi Liu, Xinying Liu, Xinyong Liu, Xinyu Liu, Xinyue Liu, Xiong Liu, Xiqiang Liu, Xiru Liu, Xishan Liu, Xiu Liu, Xiufen Liu, Xiufeng Liu, Xiuheng Liu, Xiuling Liu, Xiumei Liu, Xiuqin Liu, Xiyong Liu, Xu Liu, Xu-Dong Liu, Xu-Hui Liu, Xuan Liu, Xuanlin Liu, Xuanyu Liu, Xuanzhu Liu, Xue Liu, Xue-Lian Liu, Xue-Min Liu, Xue-Qing Liu, Xue-Zheng Liu, Xuefang Liu, Xuejing Liu, Xuekui Liu, Xuelan Liu, Xueling Liu, Xuemei Liu, Xuemeng Liu, Xuemin Liu, Xueping Liu, Xueqin Liu, Xueqing Liu, Xueru Liu, Xuesen Liu, Xueshibojie Liu, Xuesong Liu, Xueting Liu, Xuewei Liu, Xuewen Liu, Xuexiu Liu, Xueying Liu, Xueyuan Liu, Xuezhen Liu, Xuezheng Liu, Xuezhi Liu, Xufeng Liu, Xuguang Liu, Xujie Liu, Xulin Liu, Xuming Liu, Xunhua Liu, Xunyue Liu, Xuxia Liu, Xuxu Liu, Xuyi Liu, Xuying Liu, Y H Liu, Y L Liu, Y Liu, Y Y Liu, Ya Liu, Ya-Jin Liu, Ya-Kun Liu, Ya-Wei Liu, Yadong Liu, Yafei Liu, Yajing Liu, Yajuan Liu, Yaling Liu, Yalu Liu, Yan Liu, Yan-Li Liu, Yanan Liu, Yanchao Liu, Yanchen Liu, Yandong Liu, Yanfei Liu, Yanfen Liu, Yanfeng Liu, Yang Liu, Yange Liu, Yangfan Liu, Yangfan P Liu, Yangjun Liu, Yangkai Liu, Yangruiyu Liu, Yangyang Liu, Yanhong Liu, Yanhua Liu, Yanhui Liu, Yanjie Liu, Yanju Liu, Yanjun Liu, Yankuo Liu, Yanli Liu, Yanliang Liu, Yanling Liu, Yanman Liu, Yanmin Liu, Yanping Liu, Yanqing Liu, Yanqiu Liu, Yanquan Liu, Yanru Liu, Yansheng Liu, Yansong Liu, Yanting Liu, Yanwu Liu, Yanxiao Liu, Yanyan Liu, Yanyao Liu, Yanying Liu, Yanyun Liu, Yao Liu, Yao-Hui Liu, Yaobo Liu, Yaoquan Liu, Yaou Liu, Yaowen Liu, Yaoyao Liu, Yaozhong Liu, Yaping Liu, Yaqiong Liu, Yarong Liu, Yaru Liu, Yating Liu, Yaxin Liu, Ye Liu, Ye-Dan Liu, Yehai Liu, Yen-Chen Liu, Yen-Chun Liu, Yen-Nien Liu, Yeqing Liu, Yi Liu, Yi-Chang Liu, Yi-Chien Liu, Yi-Han Liu, Yi-Hung Liu, Yi-Jia Liu, Yi-Ling Liu, Yi-Meng Liu, Yi-Ming Liu, Yi-Yun Liu, Yi-Zhang Liu, YiRan Liu, Yibin Liu, Yibing Liu, Yicun Liu, Yidan Liu, Yidong Liu, Yifan Liu, Yifu Liu, Yihao Liu, Yiheng Liu, Yihui Liu, Yijing Liu, Yilei Liu, Yili Liu, Yilin Liu, Yimei Liu, Yiming Liu, Yin Liu, Yin-Ping Liu, Yinchu Liu, Yinfang Liu, Ying Liu, Ying Poi Liu, Yingchun Liu, Yinghua Liu, Yinghuan Liu, Yinghui Liu, Yingjun Liu, Yingli Liu, Yingwei Liu, Yingxia Liu, Yingyan Liu, Yingyi Liu, Yingying Liu, Yingzi Liu, Yinhe Liu, Yinhui Liu, Yining Liu, Yinjiang Liu, Yinping Liu, Yinuo Liu, Yiping Liu, Yiqing Liu, Yitian Liu, Yiting Liu, Yitong Liu, Yiwei Liu, Yiwen Liu, Yixiang Liu, Yixiao Liu, Yixuan Liu, Yiyang Liu, Yiyi Liu, Yiyuan Liu, Yiyun Liu, Yizhi Liu, Yizhuo Liu, Yong Liu, Yong Mei Liu, Yong-Chao Liu, Yong-Hong Liu, Yong-Jian Liu, Yong-Jun Liu, Yong-Tai Liu, Yong-da Liu, Yongchao Liu, Yonggang Liu, Yonggao Liu, Yonghong Liu, Yonghua Liu, Yongjian Liu, Yongjie Liu, Yongjun Liu, Yongli Liu, Yongmei Liu, Yongming Liu, Yongqiang Liu, Yongshuo Liu, Yongtai Liu, Yongtao Liu, Yongtong Liu, Yongxiao Liu, Yongyue Liu, You Liu, You-ping Liu, Youan Liu, Youbin Liu, Youdong Liu, Youhan Liu, Youlian Liu, Youwen Liu, Yu Liu, Yu Xuan Liu, Yu-Chen Liu, Yu-Ching Liu, Yu-Hui Liu, Yu-Li Liu, Yu-Lin Liu, Yu-Peng Liu, Yu-Wei Liu, Yu-Zhang Liu, YuHeng Liu, Yuan Liu, Yuan-Bo Liu, Yuan-Jie Liu, Yuan-Tao Liu, YuanHua Liu, Yuanchu Liu, Yuanfa Liu, Yuanhang Liu, Yuanhui Liu, Yuanjia Liu, Yuanjiao Liu, Yuanjun Liu, Yuanliang Liu, Yuantao Liu, Yuantong Liu, Yuanxiang Liu, Yuanxin Liu, Yuanxing Liu, Yuanying Liu, Yuanyuan Liu, Yubin Liu, Yuchen Liu, Yue Liu, Yuecheng Liu, Yuefang Liu, Yuehong Liu, Yueli Liu, Yueping Liu, Yuetong Liu, Yuexi Liu, Yuexin Liu, Yuexing Liu, Yueyang Liu, Yueyun Liu, Yufan Liu, Yufei Liu, Yufeng Liu, Yuhao Liu, Yuhe Liu, Yujia Liu, Yujiang Liu, Yujie Liu, Yujun Liu, Yulan Liu, Yuling Liu, Yulong Liu, Yumei Liu, Yumiao Liu, Yun Liu, Yun-Cai Liu, Yun-Qiang Liu, Yun-Ru Liu, Yun-Zi Liu, Yunfen Liu, Yunfeng Liu, Yuning Liu, Yunjie Liu, Yunlong Liu, Yunqi Liu, Yunqiang Liu, Yuntao Liu, Yunuan Liu, Yunuo Liu, Yunxia Liu, Yunyun Liu, Yuping Liu, Yupu Liu, Yuqi Liu, Yuqiang Liu, Yuqing Liu, Yurong Liu, Yuru Liu, Yusen Liu, Yutao Liu, Yutian Liu, Yuting Liu, Yutong Liu, Yuwei Liu, Yuxi Liu, Yuxia Liu, Yuxiang Liu, Yuxin Liu, Yuxuan Liu, Yuyan Liu, Yuyi Liu, Yuyu Liu, Yuyuan Liu, Yuzhen Liu, Yv-Xuan Liu, Z H Liu, Z Q Liu, Z Z Liu, Zaiqiang Liu, Zan Liu, Zaoqu Liu, Ze Liu, Zefeng Liu, Zekun Liu, Zeming Liu, Zengfu Liu, Zeyu Liu, Zezhou Liu, Zhangyu Liu, Zhangyuan Liu, Zhansheng Liu, Zhao Liu, Zhaoguo Liu, Zhaoli Liu, Zhaorui Liu, Zhaotian Liu, Zhaoxiang Liu, Zhaoxun Liu, Zhaoyang Liu, Zhe Liu, Zhekai Liu, Zheliang Liu, Zhen Liu, Zhen-Lin Liu, Zhendong Liu, Zhenfang Liu, Zhenfeng Liu, Zheng Liu, Zheng-Hong Liu, Zheng-Yu Liu, ZhengYi Liu, Zhengbing Liu, Zhengchuang Liu, Zhengdong Liu, Zhenghao Liu, Zhengkun Liu, Zhengtang Liu, Zhengting Liu, Zhenguo Liu, Zhengxia Liu, Zhengye Liu, Zhenhai Liu, Zhenhao Liu, Zhenhua Liu, Zhenjiang Liu, Zhenjiao Liu, Zhenjie Liu, Zhenkui Liu, Zhenlei Liu, Zhenmi Liu, Zhenming Liu, Zhenna Liu, Zhenqian Liu, Zhenqiu Liu, Zhenwei Liu, Zhenxing Liu, Zhenxiu Liu, Zhenzhen Liu, Zhenzhu Liu, Zhi Liu, Zhi Y Liu, Zhi-Fen Liu, Zhi-Guo Liu, Zhi-Jie Liu, Zhi-Kai Liu, Zhi-Ping Liu, Zhi-Ren Liu, Zhi-Wen Liu, Zhi-Ying Liu, Zhicheng Liu, Zhifang Liu, Zhigang Liu, Zhiguo Liu, Zhihan Liu, Zhihao Liu, Zhihong Liu, Zhihua Liu, Zhihui Liu, Zhijia Liu, Zhijie Liu, Zhikui Liu, Zhili Liu, Zhiming Liu, Zhipeng Liu, Zhiping Liu, Zhiqian Liu, Zhiqiang Liu, Zhiru Liu, Zhirui Liu, Zhishuo Liu, Zhitao Liu, Zhiteng Liu, Zhiwei Liu, Zhixiang Liu, Zhixue Liu, Zhiyan Liu, Zhiying Liu, Zhiyong Liu, Zhiyuan Liu, Zhong Liu, Zhong Wu Liu, Zhong-Hua Liu, Zhong-Min Liu, Zhong-Qiu Liu, Zhong-Wu Liu, Zhong-Ying Liu, Zhongchun Liu, Zhongguo Liu, Zhonghua Liu, Zhongjian Liu, Zhongjuan Liu, Zhongmin Liu, Zhongqi Liu, Zhongqiu Liu, Zhongwei Liu, Zhongyu Liu, Zhongyue Liu, Zhongzhong Liu, Zhou Liu, Zhou-di Liu, Zhu Liu, Zhuangjun Liu, Zhuanhua Liu, Zhuo Liu, Zhuoyuan Liu, Zi Hao Liu, Zi-Hao Liu, Zi-Lun Liu, Zi-Ye Liu, Zi-wen Liu, Zichuan Liu, Zihang Liu, Zihao Liu, Zihe Liu, Ziheng Liu, Zijia Liu, Zijian Liu, Zijing J Liu, Zimeng Liu, Ziqian Liu, Ziqin Liu, Ziteng Liu, Zitian Liu, Ziwei Liu, Zixi Liu, Zixuan Liu, Ziyang Liu, Ziying Liu, Ziyou Liu, Ziyuan Liu, Ziyue Liu, Zong-Chao Liu, Zong-Yuan Liu, Zonghua Liu, Zongjun Liu, Zongtao Liu, Zongxiang Liu, Zu-Guo Liu, Zuguo Liu, Zuohua Liu, Zuojin Liu, Zuolu Liu, Zuyi Liu, Zuyun Liu
articles

Secretion and transfer of adipose lipoprotein lipase utilizes neutral sphingomyelinase 2 generated exosomes.

Terri A Pietka, Edward F Morris, Megan Basco +7 more · 2025 · bioRxiv : the preprint server for biology · Cold Spring Harbor Laboratory · added 2026-04-24
Lipoprotein lipase (LPL) is critical for clearance of circulating triglycerides and for tissue fatty acid supply. LPL is primarily synthesized and secreted by adipocytes into the interstitium and must Show more
Lipoprotein lipase (LPL) is critical for clearance of circulating triglycerides and for tissue fatty acid supply. LPL is primarily synthesized and secreted by adipocytes into the interstitium and must traffic from there to the abluminal/basolateral side of capillary endothelial cells. There, LPL binds glycosylphosphatidylinositol-anchored protein 1, GPIHBP1, which stabilizes the protein and facilitates its movement across the endothelial cells to the luminal side where it functions in hydrolysis of lipoprotein triglycerides. Importance of LPL traffic is supported by findings that rare mutations in GPIHBP1 cause hypertriglyceridemia. However our understanding of how LPL is secreted by adipocytes and traffics to endothelial cells is incomplete. Here we examined the possibility that secretion and traffic of adipocyte LPL might involve generation of small extracellular vesicles (sEVs/exosomes) which often mediate cell-cell communication. Proteomic analysis of sEVs secreted by adipocytes showed them enriched in LPL. To study LPL secretion and transfer we generated human derived pre-adipocytes (HPA) that stably express tagged LPL (FLAG and His epitopes). LPL pulldown and sEV isolation from HPA conditioned media documented that greater than 70% of secreted LPL is present in sEVs. The mechanism for LPL secretion in sEVs was found to involve the ESCRT-independent neutral sphingomyelinase 2 (nSMase2) pathway, as treatment with the nSMase2 inhibitor GW4869 reduced secretion by 80%. The above observations were reproduced using highly sensitive nanoparticle flow cytometry. The sEV associated LPL has lipolytic activity and it is released by heparin addition indicating it is on the sEV surface. In addition, using human derived microvascular endothelial cells with stable lentiviral expression of GPIHBP1 we show that LPL positive sEVs transfer LPL to these cells, but not to control cells without GPIHBP1. Our findings suggest that sEV formation by nSMase2 controls adipocyte LPL secretion and traffic, that sEVs protect LPL activity and facilitate LPL transfer to GPIHBP1 on endothelial cells. Show less
📄 PDF DOI: 10.1101/2025.07.31.665751
LPL

Schisandrin B alleviates metabolic associated fatty liver disease by regulating the PPARγ signaling pathway and gut microbiota in mice.

Jiuchen Wan, Chenjian Lang, Meng Gao +5 more · 2025 · Frontiers in pharmacology · Frontiers · added 2026-04-24
The aim of this study was to investigate the improving effect of Schisandrin B (Sch B) on metabolic associated fatty liver disease (MAFLD) by regulating the PPARγ signaling pathway and gut microbiota, Show more
The aim of this study was to investigate the improving effect of Schisandrin B (Sch B) on metabolic associated fatty liver disease (MAFLD) by regulating the PPARγ signaling pathway and gut microbiota, and its mechanism in mice. Male C57BL/6 mice were fed with a high-fat diet (HFD) continuously for 16 weeks to establish a MAFLD model. The levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), interleukin-10 (IL-10), and lipopolysaccharide (LPS) in serum, as well as the level of malondialdehyde (MDA), and the activities of glutathione peroxidase (GSH-Px) and superoxide dismutase (SOD) in the liver tissue were measured. Changes in the gut microbiota of mice was analyzed by 16S rRNA sequencing technology. The expression levels of PPARγ, Plin2, Pck1, Acsl4, and Fads1 proteins, as well as those of zonula occludins 1 (ZO-1) and Occludin proteins in the colon tissue were detected by Western Blot. The results showed that Sch B could alleviate the structure disorder, ballooning degeneration, inflammatory cell infiltration, liver lipid droplets, and fibrosis in liver tissue, lower the levels of AST, ALT, TG, TC, LDL-C, and LPS, increase the level of HDL-C and lower the levels of TNF-α and IL-6 in serum, increase the level of IL-10, and lower the level of MDA and increase the activities of SOD and GSH-Px in liver tissue in MAFLD mice. Sch B could increase the expression levels of PPARγ, Pck1, and Fads1 proteins, but decrease Plin2 and Acsl4 proteins in liver tissue. Sch B could improve the diversity and abundance of the gut microbiota, restore the normal composition of the gut microbiota at the phylum and genus levels, alleviate the disruption of the gut barrier caused by HFD, and enhance the expression of ZO-1 and Occludin proteins in colon tissue in MAFLD mice. This study showed Sch B can improve HFD-induced MAFLD, and the mechanism may be through regulating the PPARγ, Plin2, PCk1, Acsl4 and Fads1 signaling pathway, restoring the diversity of gut microbiota, and improving the gut barrier to delay the progression of MAFLD. Show less
📄 PDF DOI: 10.3389/fphar.2025.1583307
FADS1

Prenatal androgen exposure delays puberty onset in female offspring rats: possible roles of leptin and α-MSH.

Runfei Ge, Yongting Yuan, Jingqi Liu +7 more · 2025 · Endocrine · Springer · added 2026-04-24
To clarify the possible mechanism of leptin and α-MSH on the onset of puberty in female offspring rats after prenatal androgen exposure. Sixteen 8-week-old specific pathogen free (SPF) healthy Sprague Show more
To clarify the possible mechanism of leptin and α-MSH on the onset of puberty in female offspring rats after prenatal androgen exposure. Sixteen 8-week-old specific pathogen free (SPF) healthy Sprague Dawley (SD) pregnant rats were randomly divided into the testosterone-treated group (TG, female offspring termed PNA group) or the olive oil control group (OOG, female offspring termed VEH group). The female offspring rats of two groups were raised to 21 days (PND21) and weaned. Six female offspring rats at PND21 (VEH:PNA = 3:3) were randomly selected for transcriptome sequencing. Twenty-seven offspring female rats were randomly divided into three groups (VEHI:VEHII:PNA = 9:9:9). VEHI group was observed until the onset of puberty, VEHII and PNA groups were observed until the 8th week. Compared with VEH group, onset of puberty was not observed in PNA group, and hypothalamic Pomc gene expression at PND21 was lower. Compared with the VEHI group, the body weight, abdominal fat, serum testosterone (T), dehydroepiandrosterone (DHEA) and leptin (LEP) levels were upregulated in the PNA group, while serum gonadotropin-releasing hormone (GnRH), mRNA of hypothalamic estrogen receptor α (ERα), α-melanocyte stimulating hormone (α-MSH), melanocortin receptor-4 (MC4R), GnRH and adipose AR, and the protein of androgen receptor (AR) and leptin receptor (LEPR) in the hypothalamic arcuate nucleus (ARC) were decreased. In the PNA group, there were positive correlations between serum DHEA and mRNA of hypothalamic ERα, MC4R and AR, negative correlations between mRNA of adipose AR and serum T and free testosterone (FT). Prenatal androgen exposure delayed the onset of puberty in female offspring, the possible mechanism of which is that prenatal androgen exposure may increase the levels of androgen and LEP, decreases their sensitivity and the expression of AR, LEPR, and MC4R, reducing GnRH secretion. Show less
📄 PDF DOI: 10.1007/s12020-025-04388-4
MC4R

FGF22 Secreted by Hair Papilla Cells Regulates Hair Follicle Stem Cell Proliferation and Differentiation.

Yu Luo, Tong Xiao, Binpeng Xi +5 more · 2025 · Biomolecules · MDPI · added 2026-04-24
Hair follicle stem cells (HFSCs) are resident stem cells within hair follicles (HFs) that possess self-renewal and differentiation capacities, serving as a critical model for regenerative medicine res Show more
Hair follicle stem cells (HFSCs) are resident stem cells within hair follicles (HFs) that possess self-renewal and differentiation capacities, serving as a critical model for regenerative medicine research. Their dynamic interaction with dermal papilla cells (DPCs) plays a decisive role in HF development and cycling. Show less
📄 PDF DOI: 10.3390/biom15111560
FGFR1

Family caregivers' needs at 6 months after pancreatic cancer surgery: A latent profile analysis.

Linglong Liu, Xiaoping Fang, Xinbo Wang +8 more · 2025 · International journal of nursing studies advances · Elsevier · added 2026-04-24
Family caregivers ('carers') bear the highest care burden during the postoperative survivorship period of pancreatic cancer, given its poor prognosis. Most carers report unmet needs when taking on car Show more
Family caregivers ('carers') bear the highest care burden during the postoperative survivorship period of pancreatic cancer, given its poor prognosis. Most carers report unmet needs when taking on caregiving responsibilities during this period. Thoroughly investigating carers' needs is essential for helping families address practical care challenges. However, this important topic remains underexplored. To assess the need levels and identify need subgroups among carers of patients with pancreatic cancer 6 months after surgery and demographic predictors contributing to heterogeneity. Cross-sectional study. Participants were recruited from the pancreas centres of four tertiary A-level comprehensive hospitals in Jiangsu Province, China. 240 patients with pancreatic cancer and their carers ('dyads') participated in the survey. Carers completed the Comprehensive Needs Assessment Tool in Cancer for Carers, the Activities of Daily Living Scale for patients, and the General Demographic Information Questionnaire for dyads. Latent profile analysis (LPA) was used to categorise carers' needs. Non-parametric and chi-square tests were used to examine differences in need scores and sociodemographic characteristics among subgroups. Multiple logistic regression (MLR) was used to analyse sociodemographic impacts. Six months post-surgery, the total carers' need score was 41.83 ± 22.65 points, indicating a moderate level, with the highest needs reported for healthcare personnel, information and knowledge, and facilities and services. The LPA results revealed that carers were divided into five distinct subgroups based on differing levels of need across the domains assessed by the Comprehensive Needs Assessment Tool in Cancer for Carers, with proportions of 8.8 %, 22.5 %, 8.3 %, 55 %, and 5.4 %. Subgroup membership was predicted by four factors: carers' sex (odds ratio [OR]: 11.08, 95 % confidence interval [CI]: 1.64, 74.99, We have highlighted the complex individualised needs of carers of patients with pancreatic cancer. Through LPA and MLR, we identified distinct need subgroups and their predictors. Healthcare professionals may be able to improve dyads' health by tailoring support to each subgroup's specific needs and issues. Registration number: ChiCTR2400079415, registered 03/01/2024, first recruitment 04/02/2024. Show less
📄 PDF DOI: 10.1016/j.ijnsa.2025.100416
LPA

Endothelial cells-derived SEMA3G suppresses glioblastoma stem cells by inducing c-Myc degradation.

Peng-Xiang Min, Li-Li Feng, Yi-Xuan Zhang +12 more · 2025 · Cell death and differentiation · Nature · added 2026-04-24
The poor prognosis of glioblastoma (GBM) patients is attributed mainly to abundant neovascularization and presence of glioblastoma stem cells (GSCs). GSCs are preferentially localized to the perivascu Show more
The poor prognosis of glioblastoma (GBM) patients is attributed mainly to abundant neovascularization and presence of glioblastoma stem cells (GSCs). GSCs are preferentially localized to the perivascular niche to maintain stemness. However, the effect of abnormal communication between endothelial cells (ECs) and GSCs on GBM progression remains unknown. Here, we reveal that ECs-derived SEMA3G, which is aberrantly expressed in GBM patients, impairs GSCs by inducing c-Myc degradation. SEMA3G activates NRP2/PLXNA1 in a paracrine manner, subsequently inducing the inactivation of Cdc42 and dissociation of Cdc42 and WWP2 in GSCs. Once released, WWP2 interacts with c-Myc and mediates c-Myc degradation via ubiquitination. Genetic deletion of Sema3G in ECs accelerates GBM growth, whereas SEMA3G overexpression or recombinant SEMA3G protein prolongs the survival of GBM bearing mice. These findings illustrate that ECs play an intrinsic inhibitory role in GSCs stemness via the SMEA3G-c-Myc distal regulation paradigm. Targeting SEMA3G signaling may have promising therapeutic benefits for GBM patients. Show less
no PDF DOI: 10.1038/s41418-025-01534-3
WWP2

GPIHBP1 increase accounts for rheumatic arthritis-related hypotriglyceridemia by facilitating lipids uptake of white adipose tissues.

Meng-Ke Song, Meng-Fan Gu, Ling Liu +7 more · 2025 · Arthritis research & therapy · BioMed Central · added 2026-04-24
Metabolism alteration is a common complication of rheumatic arthritis (RA). This work investigated the reason behind RA-caused triglyceride (TG) changes. Fresh RA patients' whole blood was transfused Show more
Metabolism alteration is a common complication of rheumatic arthritis (RA). This work investigated the reason behind RA-caused triglyceride (TG) changes. Fresh RA patients' whole blood was transfused into NOD-SCID mice. Metabolism-regulatory tissues were examined after sacrifice. To verify the findings, tissues of the rats with long-lasting adjuvant-induced arthritis (AIA) were analyzed. Some rats were injected with human plasma and GPIHBP1, and their blood TG was monitored. Various cells were stimulated by cytokines or rheumatic subjects' serum. Some pre-adipocytes were cultured by human serum or in the presence of HUVEC cells and GPIHBP1. TG decrease occurred in blood and white adipose tissues (WAT) of the RA blood-transfused NOD-SCID mice and chronic AIA rats. Fatty acids (FA) oxidation in muscles was accelerated a bit, while TG catabolism status in their livers was varied. TNF-α, IL-1β, IL-6 and RA/AIA serum promoted expression of TG utilization-related enzymes and FA uptake transporters in pre-adipocytes, but barely affected LPL. Mild IL-6 stimulus promoted GPIHBP1 release of HUVEC cells. GPIHBP1 was increased in RA serum. This change can decrease blood TG in rats, which was overshadowed by an injection of excessive GPIHBP1. RA serum slightly inhibited LPL secretion in pre-adipocytes. Both HUVEC cells co-culture and GPIHBP1 supplement reduced LPL distribution on pre-adipocytes, and eliminated LPL activity difference between normal and RA serum-treated cells. No TG uptake difference was observed in these circumstances. RA-associated inflammation induces GPIHBP1 secretion of endothelial cells, which facilitates blood TG hydrolysis and uptake to compensate the loss in WAT. Show less
📄 PDF DOI: 10.1186/s13075-025-03483-1
LPL

Whole genome sequencing analysis of body mass index identifies novel African ancestry-specific risk allele.

Xinruo Zhang, Jennifer A Brody, Mariaelisa Graff +122 more · 2025 · Nature communications · Nature · added 2026-04-24
Xinruo Zhang, Jennifer A Brody, Mariaelisa Graff, Heather M Highland, Nathalie Chami, Hanfei Xu, Zhe Wang, Kendra R Ferrier, Geetha Chittoor, Navya Shilpa Josyula, Mariah Meyer, Shreyash Gupta, Xihao Li, Zilin Li, Matthew A Allison, Diane M Becker, Lawrence F Bielak, Joshua C Bis, Meher Preethi Boorgula, Donald W Bowden, Jai G Broome, Erin J Buth, Christopher S Carlson, Kyong-Mi Chang, Sameer Chavan, Yen-Feng Chiu, Lee-Ming Chuang, Matthew P Conomos, Dawn L DeMeo, Mengmeng Du, Ravindranath Duggirala, Celeste Eng, Alison E Fohner, Barry I Freedman, Melanie E Garrett, Xiuqing Guo, Chris Haiman, Benjamin D Heavner, Bertha Hidalgo, James E Hixson, Yuk-Lam Ho, Brian D Hobbs, Donglei Hu, Qin Hui, Chii-Min Hwu, Rebecca D Jackson, Deepti Jain, Rita R Kalyani, Sharon L R Kardia, Tanika N Kelly, Ethan M Lange, Michael LeNoir, Changwei Li, Loic Le Marchand, Merry-Lynn N McDonald, Caitlin P McHugh, Alanna C Morrison, Take Naseri, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Jeffrey O'Connell, Christopher J O'Donnell, Nicholette D Palmer, James S Pankow, James A Perry, Ulrike Peters, Michael H Preuss, D C Rao, Elizabeth A Regan, Sefuiva M Reupena, Dan M Roden, Jose Rodriguez-Santana, Colleen M Sitlani, Jennifer A Smith, Hemant K Tiwari, Ramachandran S Vasan, Zeyuan Wang, Daniel E Weeks, Jennifer Wessel, Kerri L Wiggins, Lynne R Wilkens, Peter W F Wilson, Lisa R Yanek, Zachary T Yoneda, Wei Zhao, Sebastian Zöllner, Donna K Arnett, Allison E Ashley-Koch, Kathleen C Barnes, John Blangero, Eric Boerwinkle, Esteban G Burchard, April P Carson, Daniel I Chasman, Yii-der Ida Chen, Joanne E Curran, Myriam Fornage, Victor R Gordeuk, Jiang He, Susan R Heckbert, Lifang Hou, Marguerite R Irvin, Charles Kooperberg, Ryan L Minster, Braxton D Mitchell, Mehdi Nouraie, Bruce M Psaty, Laura M Raffield, Alexander P Reiner, Stephen S Rich, Jerome I Rotter, M Benjamin Shoemaker, Nicholas L Smith, Kent D Taylor, Marilyn J Telen, Scott T Weiss, Yingze Zhang, Nancy Heard-Costa, Yan V Sun, Xihong Lin, L Adrienne Cupples, Leslie A Lange, Ching-Ti Liu, Ruth J F Loos, Kari E North, Anne E Justice Show less
Obesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data fr Show more
Obesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51% were of non-European population groups. We discovered 18 BMI-associated signals (P < 5 × 10 Show less
no PDF DOI: 10.1038/s41467-025-58420-2
POC5

Advancing the clinical assessment of glomerular podocyte pathology in kidney biopsies via super-resolution microscopy and angiopoietin-like 4 staining.

Xiaojing Liu, Suxia Wang, Gang Liu +7 more · 2025 · Theranostics · added 2026-04-24
📄 PDF DOI: 10.7150/thno.101498
ANGPTL4

Predictors of Transition from Mild Cognitive Impairment to Normal Cognition and Dementia.

Jiage Gao, Lin Liu, Zifeng Yang +2 more · 2025 · Behavioral sciences (Basel, Switzerland) · MDPI · added 2026-04-24
Mild cognitive impairment (MCI) represents a heterogeneous state between normal aging and dementia, with varied transition pathways. While factors influencing MCI progression are known, their role in Show more
Mild cognitive impairment (MCI) represents a heterogeneous state between normal aging and dementia, with varied transition pathways. While factors influencing MCI progression are known, their role in cognitive reversal is unclear. This study analyzed 756 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants, classified as progressive MCI (pMCI, N = 272, mean age = 75.10 ± 7.34 years), reversible MCI (rMCI, N = 52, mean age = 69.94 ± 7.98 years) and stable MCI (sMCI, N = 432, mean age = 73.34 ± 7.44 years) based on 36-month follow-up. We compared demographic, lifestyle, clinical, cognitive, neuroimaging, and biomarker data across groups and developed a prediction model. Patients in the rMCI group were significantly younger and had a higher level of education compared with those in the pMCI group. Memory, general cognition, daily functional activities, and hippocampal volume effectively distinguished all three groups. In contrast, Aβ, tau, and other brain regions were able to distinguish only between progressive and non-progressive cases. Informant-reported Everyday Cognition (Ecog) scales outperformed self-reported Ecog scales in differentiating subtypes and predicting progression. Multinomial regression revealed that higher education, larger hippocampal volume, and lower daily functional impairment were associated with reversion, whereas Show less
📄 PDF DOI: 10.3390/bs15111552
APOE

Genome-wide analysis identifies susceptibility loci for heart failure and nonischemic cardiomyopathy subtype in the East Asian populations.

Yi Han, Yun Hong, Yan Gao +11 more · 2025 · PLoS genetics · PLOS · added 2026-04-24
Heart failure (HF) is a serious cardiovascular condition resulting from abnormalities in multiple biological processes, affecting over 64 million people worldwide. We sought to expand our understandin Show more
Heart failure (HF) is a serious cardiovascular condition resulting from abnormalities in multiple biological processes, affecting over 64 million people worldwide. We sought to expand our understanding of the genetic basis of HF and more specific NICM subtype in the East Asian populations and evaluate the biological pathways underlying subclinical left ventricular dysfunction. We conducted a meta-analysis of genome-wide association studies (GWAS) for all-cause HF in the East Asian populations (N cases ~ 13,385) and a more precise definition of nonischemic cardiomyopathy (NICM) subtype in multi-ancestry populations (N cases~3,603). We identified a low-frequency East-Asian enriched coding variant near MYBPC3 and a NICM specific locus. Follow up analyses demonstrated male-specific HF association at the MYBPC3 locus, and highlighted SVIL as a candidate causal gene for NICM. Moreover, we demonstrated that SVIL deficiency aggravated cardiomyocyte hypertrophy, apoptosis and impaired cell viability in phenylephrine (PE)-treated H9C2 cells. In addition, the gene expression level of B-type natriuretic peptide (BNP) which was deemed as a hallmark for HF was further elevated by SVIL silencing in PE-stimulated H9C2 cells. RNA-sequencing analysis of H9C2 cells revealed that the function of SVIL might be mediated through pathways relevant to regulation and differentiation of heart muscle. These results enhance our understanding of the genetic architecture of HF in the East Asian populations, and provide important insight into the biological pathways underlying NICM and sex-specific relevance of the MYBPC3 locus that warrants further replication in another datasets. Show less
📄 PDF DOI: 10.1371/journal.pgen.1011897
MYBPC3

Serum BDNF helps identify favorable subgroups in HCC patients treated with PD-L1 inhibitors and anti-angiogenic TKIs.

Caifeng Gong, Jinglong Huang, Dandan Cao +10 more · 2025 · Therapeutic advances in medical oncology · SAGE Publications · added 2026-04-24
Immune checkpoint inhibitors (ICIs) combined with antiangiogenic agents have become a standard strategy for advanced hepatocellular carcinoma (HCC). There remains an urgent need for effective biomarke Show more
Immune checkpoint inhibitors (ICIs) combined with antiangiogenic agents have become a standard strategy for advanced hepatocellular carcinoma (HCC). There remains an urgent need for effective biomarkers to guide treatment, with C-reactive protein and alpha-fetoprotein in immunotherapy (CRAFITY) scores and cytokine levels representing promising candidates. We aimed to assess the efficacy, safety, and potential biomarkers of anlotinib plus TQB2450 in patients with advanced HCC. This study was a single-arm, phase Ib trial. Twenty-five patients with advanced HCC were enrolled. Patients received an intravenous infusion of TQB2450 (1200 mg, on Day 1) and oral administration of anlotinib (initiated at 10 mg, once a day, from Day 1 to Day 14), which was repeated every 3 weeks. Blood was collected at baseline for serum cytokine analysis. After a median follow-up of 41.80 months, the median progression-free survival (mPFS) was 5.49 months, and the median overall survival (mOS) was 8.94 months. Treatment-related adverse events (TRAEs) occurred in 22 patients, with grade ⩾3 TRAEs observed in 12 patients. Patients who achieved clinical benefit (CB) had higher baseline serum brain-derived neurotrophic factor (BDNF) levels than non-CB patients (median, 227.97 vs 129.26 pg/ml, Anlotinib plus TQB2450 demonstrated promising efficacy with manageable safety in advanced HCC. Elevated serum BDNF levels might serve as a potential positive prognostic marker and, together with ECOG score, may help complement the CRAFITY score in identifying subgroups that could benefit from ICIs and antiangiogenic therapy. Show less
📄 PDF DOI: 10.1177/17588359251407052
BDNF

Qingre Sanjie Formula alleviates atherosclerosis by promoting LXR-α/ABCG5/G8-mediated reverse cholesterol transport and bile acid synthesis.

Xiao Li, Xianglong Huang, Keyan Song +5 more · 2025 · Phytomedicine : international journal of phytotherapy and phytopharmacology · Elsevier · added 2026-04-24
Atherosclerosis is the leading cause of cardiovascular disease-related morbidity and mortality. The traditional Chinese medicine Qingre Sanjie Formula (QRSJF), composed of Prunellae Spica, Sargassum, Show more
Atherosclerosis is the leading cause of cardiovascular disease-related morbidity and mortality. The traditional Chinese medicine Qingre Sanjie Formula (QRSJF), composed of Prunellae Spica, Sargassum, Fritillariae Thunbergii Bulbus, Leonuri Herba, and Forsythiae Fructus, has shown efficacy in treating cardiovascular diseases, although its mechanisms are unclear. This study aimed to explore the protective effects of QRSJF against atherosclerosis and the mechanisms involved. The composition of QRSJF was analyzed using Ultra Performance Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry. An 8-week high-fat diet (HFD)-induced atherosclerosis model was established in ApoE Both low- and high-dose QRSJF effectively attenuated dyslipidemia and decreased serum inflammatory cytokine levels in HFD-fed ApoE QRSJF improves dyslipidemia and reduces atherosclerotic plaque in ApoE Show less
no PDF DOI: 10.1016/j.phymed.2025.156691
NR1H3

AAV9-mediated MYBPC3 gene therapy with optimized expression cassette enhances cardiac function and survival in MYBPC3 cardiomyopathy models.

Amara Greer-Short, Anna Greenwood, Elena C Leon +27 more · 2025 · Nature communications · Nature · added 2026-04-24
Hypertrophic cardiomyopathy (HCM) affects approximately 600,000 people in the United States. Loss-of-function mutations in Myosin Binding Protein C3, MYBPC3, are the most common genetic cause of HCM, Show more
Hypertrophic cardiomyopathy (HCM) affects approximately 600,000 people in the United States. Loss-of-function mutations in Myosin Binding Protein C3, MYBPC3, are the most common genetic cause of HCM, with the majority of mutations resulting in haploinsufficiency. To restore cardiac MYBPC3, we use an adeno-associated virus (AAV9) vector and engineer an optimized expression cassette with a minimal promoter and cis-regulatory elements (TN-201) to enhance packaging efficiency and cardiomyocyte expression. Rather than simply preventing cardiac dysfunction preclinically, we demonstrate in a symptomatic MYBPC3-deficient murine model the ability of AAV gene therapy to reverse cardiac hypertrophy and systolic dysfunction, improve diastolic dysfunction, and prolong survival. Dose-ranging efficacy studies exhibit restoration of wild-type MYBPC3 protein levels and saturation of cardiac improvement at the clinically relevant dose of 3E13 vg/kg, outperforming a previously published construct. These findings suggest that TN-201 may offer therapeutic benefits in MYBPC3-associated cardiomyopathy, pending further validation in clinical settings. Show less
📄 PDF DOI: 10.1038/s41467-025-57481-7
MYBPC3

TMEM106A mediates atherosclerosis progression through macrophage-centered immune responses and chemokine signaling.

Menglong Gao, Xingbang Liu, Zhen Fang +5 more · 2025 · Frontiers in immunology · Frontiers · added 2026-04-24
Atherosclerosis (AS) remains a leading cause of cardiovascular morbidity and mortality, characterized by intricate interactions between immune dysregulation and lipid metabolism abnormalities-identify Show more
Atherosclerosis (AS) remains a leading cause of cardiovascular morbidity and mortality, characterized by intricate interactions between immune dysregulation and lipid metabolism abnormalities-identifying key mediators in its pathogenesis is critical for improving diagnostics and therapies. This study focuses on Transmembrane Protein 106A (TMEM106A) to clarify its role and clinical relevance in AS progression. Public transcriptomic datasets (GSE43292, GSE100927, GSE28829) were analyzed to assess TMEM106A expression and diagnostic value; single-cell RNA-seq data (GSE159677) defined its cellular localization. Immune infiltration (ssGSEA, Cibersort, xCell) and CellChat (intercellular communication) analyses explored its immune associations. TMEM106A was significantly upregulated in AS samples across datasets, with strong diagnostic efficacy (AUC 0.80-0.95). Single-cell analysis confirmed its specific enrichment in macrophages, with functional links to immune-related pathways. TMEM106A promoted macrophage infiltration, foam cell formation, oxidative stress, and inflammatory responses, while regulating PLCB2 in chemokine signaling; silencing TMEM106A alleviated these pro-atherosclerotic effects. TMEM106A contributes to AS progression by modulating macrophage-mediated immune responses and chemokine signaling, as validated in experimental models. These findings support its potential as a clinically relevant biomarker and promising therapeutic target for AS intervention. Show less
📄 PDF DOI: 10.3389/fimmu.2025.1681645
APOE

A Novel Approach for the Early Identification of Genetic Risk Factors for Alzheimer's Disease Using EEG and Psychometric Data.

Shyamal Y Dharia, Qian Liu, Stephen D Smith +1 more · 2025 · IEEE journal of biomedical and health informatics · IEEE · added 2026-04-24
Alzheimer's disease (AD) is a progressive neurodegenerative disorder associated with impairments in memory and executive functions. Despite significant advancements in identifying genetic risk factors Show more
Alzheimer's disease (AD) is a progressive neurodegenerative disorder associated with impairments in memory and executive functions. Despite significant advancements in identifying genetic risk factors, the high cost and limited accessibility of genetic testing remain major barriers. In this work, we propose a cost-effective screening approach that leverages EEG recordings and psychometric test scores to predict an individual's genetic risk for AD. Our Convolutional Neural Network (CNN) model shows promising performance: it achieved an F1 score of 72.21% in distinguishing APOE-ϵ4/PICALM GG non-carriers (N) from APOE-ϵ4 carriers with the risky PICALM GG alleles (A+P+). It reached an F1 score of 60.78% for differentiating non-carriers (N) from APOE-ϵ4 carriers without the risky alleles (A+P-), and 65.12% when separating A+P- from A+P+. To enhance interpretability, we employ Grad-CAM, which reveals that EEG features contribute more significantly to gene prediction than psychometric measures. Notably, our model also identifies three key psychometric tests, MINI COPE (which assesses emotional coping skills), the California Verbal Learning Test (CVLT), and NEO Neuroticism, as associated with higher AD risk, consistent with prior research. Moreover, our results align with earlier findings reporting increased theta-band power among high-risk individuals. Finally, Higuchi Fractal Dimension (HFD) features drove most of the EEG-based prediction capability, as shown through our ablation study. This study highlights the potential of integrating neurophysiological and cognitive assessments to develop accessible and reliable screening tools for AD genetic risk, enabling earlier diagnoses. The code has been released at https://github.com/ Shyamal-Dharia/EEG-Psycho-Genes-AD. Show less
no PDF DOI: 10.1109/JBHI.2025.3639217
APOE

Dietary Se-enrich Cardamine violifolia supplementation decreases lipid deposition and improves antioxidant status in the liver of aging laying hens.

Mengke Yan, Xin Cong, Hui Wang +7 more · 2025 · Poultry science · Elsevier · added 2026-04-24
Aging-related lipid metabolic disorder is related to oxidative stress. Selenium (Se)-enriched Cardamine violifolia (SEC) is known for its excellent antioxidant function. The objective of this study wa Show more
Aging-related lipid metabolic disorder is related to oxidative stress. Selenium (Se)-enriched Cardamine violifolia (SEC) is known for its excellent antioxidant function. The objective of this study was to evaluate the effects of SEC on antioxidant capacity and lipid metabolism in the liver of aged laying hens. A total of 450 sixty-five-wk-old Roman laying hens were randomly divided into 5 treatments: a basal diet (without Se supplementation, CON) and basal diets supplemented with 0.3 mg/kg Se from sodium selenite (SS), 0.3 mg/kg Se from Se-enriched yeast (SEY), 0.3 mg/kg Se from SEC (SEC), or 0.3 mg/kg Se from SEC and 0.3 mg/kg Se from SEY (SEC + SEY). The experiment lasted for 8 wk. The results showed that dietary SEC + SEY supplementation decreased (P < 0.05) triglyceride (in the plasma and liver) and total cholesterol levels (in the plasma), and increased (P < 0.05) HDL-C concentration in plasma compared to CON diet. Compared with CON diet, SEC and/or SEY supplementation decreased (P < 0.05) the mRNA expression of hepatic ACC, FAS and HMGCR, and increased (P < 0.05) PPARα, VTG-II, Apo-VLDL II and ApoB expression. Dietary SEC + SEY and SEY supplementation increased (P < 0.05) Se content in egg yolk and breast muscle compared to CON diet. Dietary SEC, SEY or SEC + SEY supplementation increased (P < 0.05) the activity of antioxidant enzymes (GSH-PX, T-AOC and T-SOD) in the plasma and liver and decreased (P < 0.05) MDA content in the plasma compared to CON diet. Dietary Se supplementation promoted (P < 0.05) mRNA expression of Nrf2 in the liver. In contrast, dietary SEY and SEC supplementation resulted in a decrease (P < 0.05) of hepatic Keap1 mRNA expression compared to CON diet. Dietary SEC + SEY and/or SEC supplementation increased (P < 0.05) mRNA expression of Selenof, GPX1 and GPX4 in the liver compared with CON diet. In conclusion, dietary SEC (0.3 mg/kg Se) or SEC (0.3 mg/kg Se) + SEY (0.3 mg/kg Se) improved the antioxidant capacity and the lipid metabolism in the liver of aged laying hens, which might be associated with regulating Nrf2/Keap1 signaling pathway. Show less
📄 PDF DOI: 10.1016/j.psj.2024.104620
APOB

Multi-Angle Bioactivity Cartography for Computational Screening and Mechanistic Analysis of AChE Inhibitors From Yellow Gastrodia elata.

Ruijun Sun, Yuchi Zhang, Jingying Xu +7 more · 2025 · Archiv der Pharmazie · Wiley · added 2026-04-24
Acetylcholinesterase (AChE) inhibitors are crucial for the symptomatic management of Alzheimer's disease (AD), with natural products-particularly botanical sources like Yellow Gastrodia elata (YGE)-se Show more
Acetylcholinesterase (AChE) inhibitors are crucial for the symptomatic management of Alzheimer's disease (AD), with natural products-particularly botanical sources like Yellow Gastrodia elata (YGE)-serving as promising reservoirs of such inhibitors. Nevertheless, comprehensive screening and mechanistic characterization of their inhibitory potential remain limited. This study sought to identify potent AChE inhibitors from YGE, investigate their mechanisms of action, and assess their therapeutic prospects for AD. Methodologically, an integrated approach was employed, combining ultrafiltration-liquid chromatography (UF-LC) for rapid inhibitor screening, molecular docking and dynamics simulations for mechanistic insight, two-stage high-speed countercurrent chromatography for compound isolation, enzyme kinetics to delineate inhibition modalities, and network pharmacology to uncover relevant AD-related targets. The findings identified seven active constituents with notable AChE inhibition, among which parishins A and G were obtained at high purity (98.26% and 97.26%, respectively) and exhibited mixed-type inhibition with low IC Show less
no PDF DOI: 10.1002/ardp.70174
BACE1

Enhancer Extrachromosomal Circular DNA ANKRD28 Elicits Drug Resistance via POU2F2-Mediated Transcriptional Network in Multiple Myeloma.

Binzhen Chen, Jia Liu, Yaoxin Zhang +10 more · 2025 · Advanced science (Weinheim, Baden-Wurttemberg, Germany) · Wiley · added 2026-04-24
Multiple myeloma (MM) remains an incurable disease primarily due to the emergence of drug resistance, and the underlying mechanisms remain unclear. Extrachromosomal circular DNAs (eccDNAs) are prevale Show more
Multiple myeloma (MM) remains an incurable disease primarily due to the emergence of drug resistance, and the underlying mechanisms remain unclear. Extrachromosomal circular DNAs (eccDNAs) are prevalent in cancer genomes of both coding and non-coding regions. However, the role of non-coding eccDNA regions that serve as enhancers has been largely overlooked. Here, genome-wide profiling of serum eccDNAs from donors and MM patients who responded well or poorly to bortezomib-lenalidomide-dexamethasone (VRd) therapy is characterized. A high copy number of eccDNA ANKRD28 (eccANKRD28) predicts poor therapy response and prognosis but enhanced transcriptional activity. Established VRd-resistant MM cell lines exhibit a higher abundance of eccANKRD28, and CRISPR/Cas9-mediated elevation of eccANKRD28 desensitizes bortezomib and lenalidomide treatment both in vitro and in vivo. Integrated multi-omics analysis (H3K27ac ChIP-seq, scRNA-seq, scATAC-seq, CUT&Tag, et al.) identifies eccANKRD28 as an active enhancer involved in drug resistance driven by the key transcription factor, POU class 2 homeobox 2 (POU2F2). POU2F2 interacts with sequence-specific eccANKRD28 as well as RUNX1 and RUNX2 motifs to form the protein complex, which activates the promoter of oncogenes, including IRF4, JUNB, IKZF3, RUNX3, and BCL2. This study elucidates the potential transcriptional network of enhancer eccANKRD28 in MM drug resistance from a previously unrecognized epigenetic perspective. Show less
📄 PDF DOI: 10.1002/advs.202415695
ANKRD28

Global, regional, and national burden of type 2 diabetes-related diabetic kidney disease attributable to low physical activity from 1990 to 2021: a systematic analysis of the Global Burden of Disease Study 2021 with predictions to 2050.

Mei Wang, Ruihua Yan, Wenbo Xia +8 more · 2025 · Frontiers in endocrinology · Frontiers · added 2026-04-24
Low physical activity (LPA) significantly heightens the susceptibility of both type 2 diabetes mellitus (T2DM) and chronic renal disease. Nearly half of population diagnosed with T2DM globally worsen Show more
Low physical activity (LPA) significantly heightens the susceptibility of both type 2 diabetes mellitus (T2DM) and chronic renal disease. Nearly half of population diagnosed with T2DM globally worsen into diabetic kidney disease (DKD). Focusing on physically inactive populations, we aimed to comprehensively evaluate the trends over time and regional changes in T2DM-associated DKD attributable to LPA burden. We utilized data of the 2021 Global Burden of Disease (GBD) Study to initially assess the worldwide effects of T2DM-associated DKD attributable to LPA by computing the numbers and age-standardized rates (ASRs) of death, disability-adjusted life years (DALYs), years of life lost (YLLs), and years lived with disability (YLDs), categorized by subtypes in 2021. Linear regression model was applied to analyze the illness burden from 1990 to 2021. Furthermore, cluster analysis was performed to assess the regional differences in disease burden across GBD regions. Lastly, to forecast the illness burden for the next 25 years, we utilized the autoregressive Integrated Moving Average (ARIMA) and Excess Risk (ER) models. In 2021, the fatalities attributed to T2DM-related DKD attributable to LPA amounted to 30835 (95%UI: 12346-51646) cases, with 698484 (95%UI: 275039-1158032) DALYs. The ASRs of death and DALYs were 0.38 (95%UI: 0.15-0.63) and 8.19 (95%UI: 3.21-13.6) per 100000 individuals, respectively. Between 1990 and 2021, there was a notable escalation in deaths, DALYs, YLDs, and YLLs, as well as their ASRs. The highest burden was observed among males, older adults (aged 70 years and above), and middle Socio-demographic Index (SDI). Significant differences were noted in the disease burden among various regions and countries as defined by the GBD study. Predictive analyses indicate a continued escalation of this burden by the year 2050. The global impact of DKD attributable to LPA remains considerable, with significant disparities noted across different genders, ages, and regions. To mitigate this burden, it is crucial to implement effective interventions aimed at addressing physical inactivity, specifically designed for targeted demographic groups. Show less
📄 PDF DOI: 10.3389/fendo.2025.1625973
LPA

A new perspective on cognitive impairment in major depressive disorder: Changes in gut microbiota and their inflammatory regulatory mechanisms.

Xiaodong Song, Qilin Zhong, Rongxu Zhang +10 more · 2025 · Journal of affective disorders · Elsevier · added 2026-04-24
Cognitive impairments in major depressive disorder (MDD) affect patients' social functioning, with underlying mechanisms involving gut microbiota and inflammatory factors remaining unclear. The study Show more
Cognitive impairments in major depressive disorder (MDD) affect patients' social functioning, with underlying mechanisms involving gut microbiota and inflammatory factors remaining unclear. The study analyzed cognitive function, gut microbiota changes, and inflammatory factor levels in 39 unmedicated MDD patients and 41 healthy controls, employing correlation and moderation effect analysis. MDD patients scored lower than controls in cognitive functions like information processing speed, attention/vigilance, verbal learning, visual learning and social cognition. They showed reduced gut microbiota diversity and increased levels of inflammatory markers (TNF-α, IL-1, IL-6, IL-17, IL-27, IL-33). Sellimonas abundance correlated negatively with attention/vigilance, moderated by TNF-α, IL-27, and IL-33. This relationship was stronger at lower inflammation levels. MDD patients exhibit multi-domain cognitive dysfunction alongside pro-inflammatory states and disrupted gut microbiota. The abundance of Sellimonas significantly predicts attention/vigilance deficits. Inflammatory factors modulate the impact of gut microbiota on cognitive function, suggesting chronic low-grade inflammation as a key risk factor for cognitive impairment in MDD. Show less
no PDF DOI: 10.1016/j.jad.2025.119648
IL27

Targeting VSMCs to suppress macrophage-like phenotype switching that restrains atherogenesis by immunosuppressive oligodeoxynucleotide (A151) functionalized selenium nanoparticles.

Jingru Wang, Bo Yao, Yutian Zhang +13 more · 2025 · Journal of nanobiotechnology · BioMed Central · added 2026-04-24
Macrophage-like phenotype switching of vascular smooth muscle cells (VSMCs) is a crucial mechanism driving atherogenesis. Inhibition of a phenotype switch to macrophage-like cells is a promising strat Show more
Macrophage-like phenotype switching of vascular smooth muscle cells (VSMCs) is a crucial mechanism driving atherogenesis. Inhibition of a phenotype switch to macrophage-like cells is a promising strategy to prevent atherosclerosis (AS), and targeted nanotherapeutics represent one approach for implementing this strategy. To this end, we designed immunosuppressive oligodeoxynucleotide A151 functionalized selenium nanoparticles with a spearhead LacNAc (LN-A151-SeNPs) that target macrophage-like VSMCs. Nano characterization showed that the uniformity and stability of nanoparticles were optimized by modification with LacNAc and A151, resulting in an average diameter of 88.90 ± 1.45 nm, Zeta potentials of -21.1 ± 1.5 mV, a A151:Se molar ratio of 1:60 and mass ratio of 1.68:1. The effects of LN-A151-SeNPs on inhibiting VSMCs phenotype switching and attenuation of AS were investigated using [Image: see text] The online version contains supplementary material available at 10.1186/s12951-025-03925-7. Show less
📄 PDF DOI: 10.1186/s12951-025-03925-7
APOE

Integrating Precision Medicine and Digital Health in Personalized Weight Management: The Central Role of Nutrition.

Xiaoguang Liu, Miaomiao Xu, Huiguo Wang +1 more · 2025 · Nutrients · MDPI · added 2026-04-24
Obesity is a global health challenge marked by substantial inter-individual differences in responses to dietary and lifestyle interventions. Traditional weight loss strategies often overlook critical Show more
Obesity is a global health challenge marked by substantial inter-individual differences in responses to dietary and lifestyle interventions. Traditional weight loss strategies often overlook critical biological variations in genetics, metabolic profiles, and gut microbiota composition, contributing to poor adherence and variable outcomes. Our primary aim is to identify key biological and behavioral effectors relevant to precision medicine for weight control, with a particular focus on nutrition, while also discussing their current and potential integration into digital health platforms. Thus, this review aligns more closely with the identification of influential factors within precision medicine (e.g., genetic, metabolic, and microbiome factors) but also explores how these factors are currently integrated into digital health tools. We synthesize recent advances in nutrigenomics, nutritional metabolomics, and microbiome-informed nutrition, highlighting how tailored dietary strategies-such as high-protein, low-glycemic, polyphenol-enriched, and fiber-based diets-can be aligned with specific genetic variants (e.g., FTO and MC4R), metabolic phenotypes (e.g., insulin resistance), and gut microbiota profiles (e.g., Show less
📄 PDF DOI: 10.3390/nu17162695
MC4R

Genetic association of circulating lipids and lipid-lowering drug targets with vascular calcification.

Pengfei Zhang, Wenting Wang, Qian Xu +5 more · 2025 · Atherosclerosis · Elsevier · added 2026-04-24
Vascular calcification (VC) significantly increases the incidence and mortality of many diseases. The causal relationships of dyslipidaemia and lipid-lowering drug use with VC severity remain unclear. Show more
Vascular calcification (VC) significantly increases the incidence and mortality of many diseases. The causal relationships of dyslipidaemia and lipid-lowering drug use with VC severity remain unclear. This study explores the genetic causal associations of different circulating lipids and lipid-lowering drug targets with coronary artery calcification (CAC) and abdominal aortic artery calcification (AAC). We obtained single-nucleotide polymorphisms (SNPs) and expression quantitative trait loci (eQTLs) associated with seven circulating lipids and 13 lipid-lowering drug targets from publicly available genome-wide association studies and eQTL databases. Causal associations were investigated by univariable, multivariable, drug-target, and summary data-based Mendelian randomization (MR) analyses. Potential mediation effects of metabolic risk factors were evaluated. MR analysis revealed that genetic proxies for low-density lipoprotein cholesterol (LDL-C), triglycerides (TC) and Lipoprotein (a) (Lp(a)) were causally associated with CAC severity, and apolipoprotein B (apoB) level was causally associated with AAC severity. A significant association was detected between hepatic Lipoprotein(A) (LPA) gene expression and CAC severity. Colocalisation analysis supported the hypothesis that the association between LPA expression and CAC quantity is driven by different causal variant sites within the ±1 Mb flanking region of LPA. Serum calcium and phosphorus had causal associations with CAC severity. Inhibitors targeting LPA might represent CAC drug candidates. Moreover, T2DM, hypercalcemia, and hyperphosphatemia are positively causally associated with CAC severity, while chronic kidney disease and estimated glomerular filtration rate are not. Show less
no PDF DOI: 10.1016/j.atherosclerosis.2025.119136
APOB

Selenium Nanoparticles vs Selenite Fertilizers: Implications for Toxicological Profiles, Antioxidant Defense, and Ferroptosis Pathways.

Qiting Fang, Zhonghua Liu, Kaixi Wang · 2025 · Journal of agricultural and food chemistry · ACS Publications · added 2026-04-24
Selenium (Se) foliar fertilizers enhance crop nutrition and address human selenium deficiency, while improper application may lead to excessive intake and residue accumulation. Our study comprehensive Show more
Selenium (Se) foliar fertilizers enhance crop nutrition and address human selenium deficiency, while improper application may lead to excessive intake and residue accumulation. Our study comprehensively assessed the toxicity and function of novel selenium nanoparticles and traditional sodium selenite fertilizers across cell, zebrafish, and murine models. Both fertilizers enhanced antioxidant pathways at low doses, but selenium nanoparticles exhibited stronger antioxidant and ferroptosis-modulating effects with lower toxicity at a high dose. Sodium selenite increased total and lipid ROS production, leading to decreased viability of cells and increased distortion and mortality of zebrafish. In mice, sodium selenite induced hepatic toxicity and decreased GPX4. Transcriptome analysis revealed that sodium selenite downregulated c-JUN and APOA4, weakening the antioxidant defense, whereas selenium nanoparticles promoted ferroptosis resistance through FGF21. These findings suggest selenium nanoparticles as a safer alternative for Se biofortification, mitigating health risks while supporting food security and environmental sustainability. Show less
no PDF DOI: 10.1021/acs.jafc.5c02034
APOA4

Effect of cholesterol on distribution, cell uptake, and protein corona of lipid microspheres at sites of cardiovascular inflammatory injury.

Lingyan Li, Xingjie Wu, Qianqian Guo +9 more · 2025 · Journal of pharmaceutical analysis · Elsevier · added 2026-04-24
Cholesterol (CH) plays a crucial role in enhancing the membrane stability of drug delivery systems (DDS). However, its association with conditions such as hyperlipidemia often leads to criticism, over Show more
Cholesterol (CH) plays a crucial role in enhancing the membrane stability of drug delivery systems (DDS). However, its association with conditions such as hyperlipidemia often leads to criticism, overshadowing its influence on the biological effects of formulations. In this study, we reevaluated the delivery effect of CH using widely applied lipid microspheres (LM) as a model DDS. We conducted comprehensive investigations into the impact of CH on the distribution, cell uptake, and protein corona (PC) of LM at sites of cardiovascular inflammatory injury. The results demonstrated that moderate CH promoted the accumulation of LM at inflamed cardiac and vascular sites without exacerbating damage while partially mitigating pathological damage. Then, the slow cellular uptake rate observed for CH@LM contributed to a prolonged duration of drug efficacy. Network pharmacology and molecular docking analyses revealed that CH depended on LM and exerted its biological effects by modulating peroxisome proliferator-activated receptor gamma (PPAR-γ) expression in vascular endothelial cells and estrogen receptor alpha (ERα) protein levels in myocardial cells, thereby enhancing LM uptake at cardiovascular inflammation sites. Proteomics analysis unveiled a serum adsorption pattern for CH@LM under inflammatory conditions showing significant adsorption with CH metabolism-related apolipoprotein family members such as apolipoprotein A-V (Apoa5); this may be a major contributing factor to their prolonged circulation Show less
📄 PDF DOI: 10.1016/j.jpha.2024.101182
APOA5

Elevated serum lipoprotein(a) as a prognostic marker for reduced survival in pancreatic cancer.

Jinyue Liu, Yueping Jiang, Yueyi Xing +5 more · 2025 · BMC gastroenterology · BioMed Central · added 2026-04-24
This study aimed to assess the prognostic significance of serum lipoprotein(a) [Lp(a)] levels regarding overall survival (OS) and progression-free survival (PFS) among patients diagnosed with pancreat Show more
This study aimed to assess the prognostic significance of serum lipoprotein(a) [Lp(a)] levels regarding overall survival (OS) and progression-free survival (PFS) among patients diagnosed with pancreatic cancer (PC). A retrospective cohort of 364 pathologically confirmed PC patients treated at the Affiliated Hospital of Qingdao University between January 2019 and December 2022 was analyzed. The optimal cutoff for Lp(a) was identified using X-tile software, allowing categorization into high and low Lp(a) groups. To minimize selection bias, propensity score matching (PSM) was utilized. Survival outcomes were compared using Kaplan-Meier curves and log-rank tests. Cox proportional hazards models were applied to identify independent prognostic variables affecting OS and PFS. Patients with high Lp(a) had significantly shorter OS and PFS both before and after PSM (post-PSM OS: 12.28 vs. 27.67 months, P = 0.003; PFS: 7.00 vs. 11.30 months, P = 0.002). Multivariate Cox analysis confirmed high Lp(a) as an independent predictor of poor OS [HR = 2.11 (1.17-3.81), P = 0.013] and PFS [HR = 2.14 (1.20-3.83), P = 0.010]. In the surgical subgroup (n = 215), high Lp(a) was also associated with worse OS (16.43 vs. 35.47 months, P = 0.02) and PFS (8.40 vs. 11.77 months, P = 0.036). Multivariate analysis in this subgroup showed that high Lp(a) remained an independent risk factor for OS [HR = 2.82 (1.36-5.87), P = 0.006] and PFS [HR = 2.01 (1.06-3.86), P = 0.034]. Elevated serum Lp(a) is an independent predictor of reduced OS and PFS in patients with pancreatic cancer. In contrast to conventional lipid profiles, the genetic stability of Lp(a) makes it a reliable baseline prognostic marker. Show less
📄 PDF DOI: 10.1186/s12876-025-04573-9
LPA

Identifying internet addiction profiles and bridge connectors among Chinese college students and evaluating CBT vs. CBT+MBI interventions via a randomized controlled trial.

Shuhong Liang, Yaxu Yu, Shuang Liu +2 more · 2025 · Journal of behavioral addictions · added 2026-04-24
The Interaction of Person-Affect-Cognition-Execution (I-PACE) model offers a framework for understanding the interplay between cognitive, affective, and behavioral factors in internet addiction (IA). Show more
The Interaction of Person-Affect-Cognition-Execution (I-PACE) model offers a framework for understanding the interplay between cognitive, affective, and behavioral factors in internet addiction (IA). Our study aims to explore the heterogeneity of IA, identify bridge connectors, and compare the efficacy of cognitive behavioral therapy combined with mindfulness-based intervention (CBT+MBI) versus CBT alone in reducing IA levels among Chinese college students. In study 1, 1,030 Chinese college students completed assessments of IA, automatic thoughts, self-control, and anxiety. Latent profile analysis (LPA) was employed to identify distinct symptom profiles of IA across individuals. Network analysis (NA) identified bridge connectors for targeted intervention. In study 2, 36 participants randomly selected from the high IA and low IA groups of study 1 were randomly assigned to CBT+MBI, CBT alone, or a control group. The CBT+MBI group received an 8-week dual-modality intervention and the CBT alone received an 8-week CBT intervention, both designed to target the bridge connectors identified via NA in Study 1, while the control group only completed basic questionnaires. In study 1, LPA identified four subgroups: regular, at-risk, low IA, and high IA groups. NA pinpointed automatic thoughts and anxiety as bridge connectors. In study 2, targeted interventions significantly reduced college students' levels of IA. CBT+MBI resulted in greater and more sustained improvements compared to CBT alone, with effects maintained for six-month post-intervention. Our study not only reinforces the I-PACE model but also provides actionable strategies for designing evidence-based, multidimensional interventions to reduce addictive behaviors among college students. Show less
📄 PDF DOI: 10.1556/2006.2025.00086
LPA

Illuminating the FGFR fusion landscape in Chinese patients: unveiling novel molecular insights and clinical implications.

Zhuo Liu, Dandan Zhao, Baoming Wang +14 more · 2025 · The oncologist · Oxford University Press · added 2026-04-24
Despite the increasing approval and ongoing clinical trials of FGFR-targeted therapies, accurately detecting FGFR fusions remains a challenge due to limited research, low incidence rates, complex fusi Show more
Despite the increasing approval and ongoing clinical trials of FGFR-targeted therapies, accurately detecting FGFR fusions remains a challenge due to limited research, low incidence rates, complex fusion partner distribution, and unique kinase domain distribution. We conducted a multicenter study to comprehensively profile FGFR fusions in the largest Chinese pan-cancer cohort to date, comprising 118 FGFR fusions from 114 individuals. Both DNA- and RNA-based sequencing approaches were utilized to reveal novel and fundamental features of FGFR fusion. Our research reveals an incidence rate of 0.96% for FGFR rearrangements within this Chinese cohort, including a high incidence rate of FGFR fusions (40%) in parotid gland carcinoma. However, this is based on a small sample size of 5 tumors and should be interpreted cautiously pending validation in larger cohorts. We also uncovered distinct breakpoint distribution patterns across various FGFR rearrangements. For example, a primary breakpoint in intron17 of FGFR2 was predominant (21/22), while FGFR1/3 breakpoints displayed substantial diversity. For the first time, we identified "hot" breakpoints in FGFR1 intron17, exon18, and FGFR3's 3' untranslated region. These findings underline the importance of incorporating these regions in targeted sequencing to ensure comprehensive detection of FGFR1/3 fusions. Notably, we observed a predilection for intrachromosomal distribution in common FGFR1/2/3 fusions. In contrast, most novel fusions (12/15) exhibited an interchromosomal distribution pattern, indicating variations in the fusion formation mechanism. Importantly, our study demonstrates the substantial incremental value of RNA-NGS or other orthogonal methods in confirming the functionality of FGFR rearrangements initially identified by DNA sequencing. In our cohort, 46% (6/13) of rare FGFR1/2/3 fusions lacked detectable RNA transcripts; however, this does not definitively indicate non-functionality as factors such as low RNA quality, expression below detection limits, or nonsense-mediated decay may contribute. Therefore, RNA-based validation is critical for accurately identifying potentially targetable FGFR fusions and guiding therapy. Our findings offer critical novel insights into functional FGFR fusions and bear considerable clinical implications for identifying individuals whose tumors are most likely to respond favorably to FGFR-targeted therapies. Show less
📄 PDF DOI: 10.1093/oncolo/oyaf347
FGFR1

Blockage of CCL3 with neutralizing antibody reduces neuroinflammation and reverses Alzheimer disease phenotypes.

Chao Wei, Jing Liu, Bing Wu +8 more · 2025 · Brain, behavior, and immunity · Elsevier · added 2026-04-24
Accumulating evidence indicates that neuroinflammation is involved in the pathogenesis of Alzheimer's disease (AD). According to RNA sequencing and quantitative PCR (qPCR), we found that chemokine CCL Show more
Accumulating evidence indicates that neuroinflammation is involved in the pathogenesis of Alzheimer's disease (AD). According to RNA sequencing and quantitative PCR (qPCR), we found that chemokine CCL3 mRNA expression was abnormally upregulated in the brains of AD transgenic mice. Moreover, the levels of CCL3 in the serum of AD patients were significantly elevated and negatively correlated with their cognitive abilities. However, the role of CCL3 in AD neuroinflammation and pathological damages remains elusive. Using behavioral, histological, and biochemical methods, outcomes of CCL3 antibody treatment on neuropathology and cognitive deficits were studied in the APPswe/PS1dE9 mice. In the present study, we reported that CCL3 protein expression was increased in the APPswe/PS1dE9 mice, whereas blockage of CCL3 with neutralizing antibody potently inhibited CCL3 activation in the APPswe/PS1dE9 mice down to the levels of wild-type mice. Specifically, CCL3 antibody significantly improved the learning and memory abilities of APPswe/PS1dE9 mice. In addition, CCL3 antibody treatment decreased cerebral amyloid-β (Aβ) levels and plaque burden via inhibiting amyloid precursor protein (APP) processing by reducing beta-site APP cleaving enzyme 1 (BACE1) expression in the APPswe/PS1dE9 mice. We also found that CCL3 antibody treatment alleviated neuroinflammation and reduced synaptic defects in the APPswe/PS1dE9 mice. Furthermore, the activated NF-κB signaling pathway in APPswe/PS1dE9 mice was inhibited by CCL3 antibody treatment. Collectively, our findings provide evidence that CCL3 activation may contribute to the AD pathogenesis and may serve as a novel therapeutic target in the treatment of AD. Show less
no PDF DOI: 10.1016/j.bbi.2025.04.034
BACE1